(152c) Predicting the Long-Term Stability of Biologics with Short-Term Data

Understanding the long-term stability of biologics is crucial to ensure safe, effective, and cost-efficient life-saving therapeutics. Current industry and regulatory practices require arduous real-time data collection over two to three years; thus, any method to reduce this drug development bottleneck while still ensuring product quality would enhance the speed of medicine to patients. We developed a parallel pathway kinetic model, combined with Monte Carlo simulations for the generation of 95% prediction intervals, to predict the long-term (3 years) stability of biotherapeutic critical quality attributes (size variants, charge variants, purity by CE-SDS, and potency) with short term (3-6 month) data from intended, accelerated, and stressed storage conditions. We rigorously validated the model with 18 biotherapeutic drug products, comprised of IgG1 and IgG4 monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs), dual protein coformulations, and a fusion protein, including high concentration (≥ 100 mg/mL) formulations and both liquid and lyophilized presentations. For each drug product, we accurately predicted the long-term trends of multiple quality attributes using just 6 months of data. Further, we demonstrated superior stability prediction via our methods with less real-time data than standard linear regression methods. The robust and repeatable results of this work across an unprecedented suite of 18 biotherapeutic compounds suggest kinetic models with Monte Carlo simulation can predict the long-term stability of biologics with short-term data.